Hydrogels are widely used to produce biomolecular separations in electrophoretic applications, where gel morphology and charge effects combine to produce the separation. Nanocomposite gels are poised to revolutionize this field in both improved handling characteristics and improved separations. Gel morphology and charge effects have traditionally been manipulated by varying the copolymer composition, but recent reports show that novel morphological changes can also be induced in the gel using templating methods and nanoparticle addition. To aid realization of the potential for novel electrically driven separations arising from such novel morphologies, we review here advances in materials development alongside a review of the directions in biomolecule/hydrogel electrophoretic transport modeling. Models for polyelectrolyte transport that are potentially useful for understanding novel behaviors caused by gel morphology are analyzed first. With the perspective of theoretical guidance, we then survey nanocomposite hydrogel morphologies keyed by the nanoparticle and matrix. Finally, we survey as well reports of dramatic improvements in the mechanical properties that may be the key to the early adoption of these new materials in biomolecular separation applications. For modeling, we have identified the different scales involved in biomolecular transport in these materials and provided a taxonomy of transport models that emphasize the role of gel morphology in determining both polyelectrolyte mobility and diffusion. Three aspects for future work unique to nanocomposite gel materials are described: (a) descriptions of the distribution of nanoparticles within the gels; (b) descriptions of the motion of the buffer solution that may include electroosmotic effects for nanoparticles with surface charges; (c) descriptions of the motion of the polyelectrolyte molecule inside the new gel material for a given application. These aspects will help to uncover further quantitative details about the ability of these gels to be tunable for differential mass transport of a given type of molecule. Standard gels, currently, lack a broad flexibility to increase the separation of biomolecules and, in general, are not tunable.

Within the last decade, novel gel materials with a modified internal morphology have been synthesized and are available for a wide range of applications including drug delivery, separation of biomolecules with relevance in clinical diagnostics, electrokinetic pumping, and sensors 1-3 . One way to achieve this internal modification is to embed nanoparticles into the polymer matrix, so that when an electrical field is applied to the system, the presence of these particles can change the biomacromolecule transport through the gel, possibly altering the separation 4 . As a result, gaining a very deep understanding of the role that these nanoparticles can have on the separation efficiency in terms of electrophoresis techniques becomes very important. To the best of our knowledge, this contribution is the first effort that examines the role of morphology (in the form of a axially-diverging microdomain) on the prediction of optimal separation times for two protein models when they are subjected to electrophoresis. The research conducted predicts that the optimal separation time of a short, straight pore yields the best resolution in this type of electrophoretic separation.

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Cytoskeletal networks to transmission towers are comprised of slender elements. Slender filaments bend and buckle more easily than stretch. Therefore a deforming network is expected to exhaust all possible bending-based modes before engaging filament stretch. While the large-strain bending critically determines fibrous-media response, simulations use small-strain and jointed approximations. At low resolution, these approximations inflate bending resistance and delay buckling onset. The proposed string-of-continuous-beams (SOCB) approach captures 3D nonlinear Euler bending of filaments with high fidelity at low cost. Bending geometry (i.e. angles and its differentials) is solved as primary variables, to fit a 5 th order polynomial of the contour angle. Displacement, solved simultaneously as length conservation, is predicted with C3 and C6 smoothness between and within segments, using only 2 nodes. In the chosen analysis frame, in-plane and out-plane moments can be decoupled for arbitrarily-curved segments. Complex crosslink force-transfers can be specified. Simulations show that when a daughter branch is appended, the buckling resistance of a filament changes from linear to nonlinear before reversible collapse. An actin outcrop with 8 generations of mother-daughter branching produced the linear, nonlinear, and collapse regimes observed in compression experiments. ‘Collapse’ was a redistribution of outcrop forces following the buckling of few strands.

The epidemic of type 2 diabetes mellitus (T2DM) is an important global health concern. Our earlier epidemiological investigation in Pakistan prompted us to conduct a molecular investigation to decipher the differential genetic pathways of this health condition in relation to non-diabetic controls. Our microarray studies of global gene expression were conducted on the Affymetrix platform using Human Genome U133 Plus 2.0 Array along with Ingenuity Pathway Analysis (IPA) to associate the affected genes with their canonical pathways. High-throughput qRT-PCR TaqMan Low Density Array (TLDA) was performed to validate the selected differentially expressed genes of our interest, viz., ARNT, LEPR, MYC, RRAD, CYP2D6, TP53, APOC1, APOC2, CYP1B1, SLC2A13, and SLC33A1 using a small population validation sample (n = 15 cases and their corresponding matched controls). Overall, our small pilot study revealed a discrete gene expression profile in cases compared to controls. The disease pathways included: Insulin Receptor Signaling, Type II Diabetes Mellitus Signaling, Apoptosis Signaling, Aryl Hydrocarbon Receptor Signaling, p53 Signaling, Mitochondrial Dysfunction, Chronic Myeloid Leukemia Signaling, Parkinson’s Signaling, Molecular Mechanism of Cancer, and Cell Cycle G1/S Checkpoint Regulation, GABA Receptor Signaling, Neuroinflammation Signaling Pathway, Dopamine Receptor Signaling, Sirtuin Signaling Pathway, Oxidative Phosphorylation, LXR/RXR Activation, and Mitochondrial Dysfunction, strongly consistent with the evidence from epidemiological studies. These gene fingerprints could lead to the development of biomarkers for the identification of subgroups at high risk for future disease well ahead of time, before the actual disease becomes visible.

-This paper introduces an effective optimization approach to investigate the morphological effects of nanocomposite gel electrophoresis and operational parameters (see below) by integrating the numerical simulations based on finite element method and population-based search algorithm such as differential evolution. Simulations are performed to study the solute transport by Convection-Diffusion-Electromigration in a microvoid with axially varying cross-section. Morphological parameters such as channel shape and size, as well as operational parameters such as pressure gradient in the axial direction, and electric field in the orthogonal direction were considered and found to have considerable effects on the separation resolution in electrophoresis. Key observations on the most favorable hydrogel morphology for an efficient electrophoresis separation are presented.

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In the United States, type 2 diabetes mellitus (T2DM) disproportionately affects the African American (AA) community, which has not been systematically included in molecular studies of underlying mechanisms. As part of a gene expression study, we recruited cases with T2DM and matched, unaffected controls at an urban hospital in Washington, DC, with a majority AA population. Here we describe the profile of socio-demographic, behavioral, and health-related associations of the study population. Self-reported data were collected from cases with T2DM (N = 77) and age-and gender-matched controls (N = 80), ages 45 -65 years. Logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI). As expected, obesity, hypertension, and cardiovascular disease were more prevalent in cases than in controls. Tobacco smoking and working alongside other tobacco smokers were also associated with T2DM. After adjusting for covariates, current tobacco smoking remained statistically associated with the disease (OR per half pack of cigarettes 1.43, 95% CI 1.04 -1.95; p-value 0.027). HbA1c levels were elevated in T2DM cases who smoked more than a pack of cigarettes daily. These associations highlight the comorbid burdens of T2DM in an AA urban community setting and identify tobacco control as an unmet need for future prevention and control efforts.